System and method for accented modification of a language model

ABSTRACT

A system and method for a speech recognition technology that allows language models for a particular language to be customized through the addition of alternate pronunciations that are specific to the accent of the dictator, for a subset of the words in the language model. The system includes the steps of identifying the pronunciation differences that are best handled by modifying the pronunciations of the language model, identifying target words in the language model for pronunciation modification, and creating a accented speech file used to modify the language model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based on, and takes priority from, U.S. ProvisionalPatent Application Ser. No. 60/533,217, entitled, “A SYSTEM AND METHODFOR ACCENTED MODIFICATION OF LANGUAGE MODEL”, filed Dec. 31, 2003, whichapplication is incorporated by reference herein.

This application also relates to co-pending U.S. patent application Ser.No. 10/413,405, entitled, “INFORMATION CODING SYSTEM AND METHOD”, filedApr. 15, 2003; co-pending U.S. patent application Ser. No. 10/447,290,entitled, “SYSTEM AND METHOD FOR UTILIZING NATURAL LANGUAGE PATIENTRECORDS”, filed on May 29, 2003; co-pending U.S. patent application Ser.No. 10/448,317, entitled, “METHOD, SYSTEM, AND APPARATUS FORVALIDATION”, filed on May 30, 2003; co-pending U.S. patent applicationSer. No. 10/448,325, entitled, “METHOD, SYSTEM, AND APPARATUS FORVIEWING DATA”, filed on May 30, 2003; co-pending U.S. patent applicationSer. No. 10/448,320, entitled, “METHOD, SYSTEM, AND APPARATUS FOR DATAREUSE”, filed on May 30, 2003; co-pending U.S. patent application Ser.No. 10/953,448, entitled, “SYSTEM AND METHOD FOR DATA DOCUMENT SECTIONSEGMENTATIONS”, filed on Sep. 30, 2004; co-pending U.S. patentapplication Ser. No. 10/953,474, entitled, “SYSTEM AND METHOD FOR POSTPROCESSING SPEECH RECOGNITION OUTPUT”, filed on Sep. 29, 2004;co-pending U.S. patent application Ser. No. 10/953,471, entitled,“SYSTEM AND METHOD FOR MODIFYING A LANGUAGE MODEL AND POST-PROCESSORINFORMATION”, filed on Sep. 29, 2004; and co-pending U.S. patentapplication Ser. No. 10/951,291, entitled, “SYSTEM AND METHOD FORCUSTOMIZING SPEECH RECOGNITION INPUT AND OUTPUT”, filed on Sep. 27,2004, all of which co-pending applications are hereby incorporated byreference in their entirety.

BACKGROUND OF THE INVENTION

The present invention relates generally to a system and method forproducing an optimal language model for performing speech recognition.

Today's speech recognition technology enables a computer to transcribespoken words into computer recognized text equivalents. Speechrecognition is the process of converting an acoustic signal, captured bya transducive element, such as a microphone or a telephone, to a set oftext words in a document. These words can be used for numerousapplications including data entry and word processing. The developmentof speech recognition technology is primarily focused on accurate speechrecognition, which is a formidable task due to the wide variety ofpronunciations, accents, and speech characteristics of native andnon-native speakers of a particular language.

The key to speech recognition technology is the language model. Alanguage model describes the type of text the dictator will speak about.For example, speech recognition technology designed for the medicalprofession will utilize different language models for differentspecialties in medicine. In this example, a language model is created bycollecting text from doctors in each specialty area, such as radiology,oncology, etc. The type of text collected would include language andwords associated with that practice, such as diagnoses andprescriptions. Most importantly, these language models may be developedfor a regional or native language.

Today's state of the art speech recognition tools utilize a factory (orout-of-the-box) language model, which is often customized to produce asite-specific language model. A site-specific language model mightinclude, for example, the names of doctors or hospital departments of aspecific site using speech recognition technology. Unfortunately, it hasbeen found that many factory language models and site-specific languagemodels do not adequately address the problem of accented speech by agroup. An example of such a group would include United Kingdomphysicians dictating in United States hospitals using speech recognitiontechnology.

Accented speech presents especially challenging conditions for speechrecognition technology as the accented speech pronunciation of alanguage can result in misidentification and failed recognition ofwords. For example, a United Kingdom accented speaker or an Indianaccented speaker in the United States will pronounce an English word,even after living in the United States for an extended period of time,dramatically different than an United States speaker. So much so, that aspeech recognition engine using an United States language model, willmisidentify or fail to recognize the English word.

Previous efforts to solve this problem included acoustic adaptationduring individual speaker enrollment and factory language models thatcreated with alternate pronunciations for some commonly used words for aparticular application. These techniques are used to handle thepronunciation differences among varieties of speakers within the sameregion, such as southern accents and New York accents in the UnitedStates. Individual pronunciation idiosyncrasies that are subphonemic aretypically addressed through speaker enrollment and adaptation of theacoustic model before the speaker starts using the speech recognitionproduct. Some pervasive regional differences that are phonemic in natureare represented in the language model with alternative pronunciationsfor the same word. This situation applies to the classical differencessuch as “You say ‘tuh-may-toh’ and I say ‘tuh-mah-toh’”.

Unfortunately these techniques are only successful in providingrecognition of a limited number of alternative phonemic pronunciationsand require substantial time to personalize the acoustic model to anindividual. Using these techniques to control for the ubiquitouspronunciation differences between accented speech and native speechwould become costly and time consuming.

Another approach includes replacing the native acoustic models withdistinct acoustic models for a class of speakers who share pronunciationfeatures, and replacing native language models with dialect-specificlanguage models. These distinct acoustic models and dialect-specificlanguage models address the differences between the US English andUnited Kingdom English; they can be developed for any language ordialect. Not only are the distinct acoustic models and thedialect-specific language models large and cumbersome, but they alsoexhibit other undesirable results when used to accommodate accentedspeech. For example, United Kingdom English acoustic models and languagemodels have different spellings such as ‘colour’, ‘centre’, and‘oesophagus’. Further, United Kingdom English employs different speechpatterns and different vocabulary, such as different brand names formedical drugs.

Therefore, while speaker enrollment acoustic adaptation and alternatepronunciation factory language models can accommodate some level ofaccented speech, the expectation of speech recognition is significantlypoorer than if distinct acoustic models and dialect-specific languagemodels are used. Alternatively, speech recognition using distinctacoustic models and dialect-specific language models may transcribeaccented speech more accurately but it also creates transcriptions whichfail to conform to the native region's conventions of spelling,vocabulary and speech patterns. Furthermore, it is impractical andexpensive to employ a completely different set of language models for ahandful of individuals, such as a few United Kingdom physicians workingin a US hospital.

Therefore, there exists a need for a speech recognition technology thatautomatically updates a factory or site-specific language model upon useby an accented speaker with words and pronunciations corresponding tothe accented speech.

It may also be desirable to provide a speech recognition technology thatallows language models for a particular language to be customizedthrough the addition of alternate pronunciations that are specific tothe accent of a dictator, for a subset of the words in the languagemodel.

SUMMARY OF THE INVENTION

The present invention includes a system and method for modifying alanguage model such that a speech recognition engine can recognizeaccented pronunciation during automated speech recognition. The steps ofthe method may include accented speech identification, pronunciationdifferences identification, word instantiation of the pronunciationdifferences, accented speech file creation, and language modelmodification. The accented speech identification includes identifyingaccented speech pronunciations of words of a language. The pronunciationdifferences identification includes identifying pronunciationdifferences between customary speech pronunciations and the accentedspeech pronunciations. The word instantiation includes identifying, foreach of the pronunciation differences, a first list of words in thelanguage model that instantiate the pronunciation differences. Theaccented speech file creation includes selectively adding the first listof words and their accented speech pronunciations to an accented speechfile. The language model modification includes modifying the languagemodel according to the accent speech file.

Another aspect of the present invention may exclude subphonemicdifferences from the step of identifying pronunciation differences. Thepresent invention may also selectively reduce the first list to wordsthat are most frequently used in the language model, to words thatintrude on other words if they are not given accented speechpronunciations, to short words, and to words with unrecognizableaccented speech pronunciations. The modification of the language modelmay include supplementing the language model pronunciations withaccented speech pronunciations and may also include replacing thelanguage model pronunciations with accented speech pronunciations.

In another aspect of the present invention, the accented speech file maybe used for cloning. The steps may include identifying clonepronunciations between customary speech and accented speech andselectively adding the clone pronunciations to the accented speech file.

The invention also includes a system and method for customizing alanguage model for accented speakers by identifying an accent anddetermining pronunciation differences between the identified accent andthe language model. Other steps include selecting a first subset of thepronunciation differences based on a first set of pre-determinedcriteria, listing a first set of instantiations based on the firstsubset, and compiling an accent speech word list based on the first setof instantiations. The accent-specific pronunciations corresponding towords in the accent speech word list are determined and the accentedspeech word list and the accent-specific pronunciations are then appliedto the language model.

In another aspect of the present invention, the method of determiningthe pronunciation differences includes considering systematicrule-governed differences, phonemic differences, and idiosyncraticcriteria. The present invention also includes compiling the accentspeech word list based on a second set of pre-determined criteria. Thesecond set of pre-determined criteria may include at least one of wordfrequency, pronunciation intrusions, and word length. Identifyingpronunciation intrusions may be based on a third set of pre-determinedcriteria.

The present invention also includes a system and method for modifying alanguage model by identifying accented speech pronunciations of words ofa language and identifying pronunciation differences between customaryspeech pronunciations and the accented speech pronunciations. The methodmay also include identifying, for each pronunciation difference, a firstlist of words in the language model that instantiate the pronunciationdifferences. The method may selectively add the first list of words andtheir accented speech pronunciations to an accented speech file andselectively reduce the first list to a second list of words that aremost frequently used in the language model. The method may thenselectively add the second list of words and their accented speechpronunciations to the accented speech file and selectively reduce thesecond list to a third list of words, wherein the third list includeswords that intrude on other words if they are not given accented speechpronunciations. The method may also then selectively add the third listof words and their accented speech pronunciations to the accented speechfile and selectively reduce the third list to a fourth list of shortwords. The method may also then selectively add the fourth list of wordsand their accented speech pronunciations to the accented speech file andselectively reduce the fourth list to a fifth list of words withunrecognizable accented speech pronunciations. The method may thenselectively add the fifth list of words and their accented speechpronunciations to the accented speech file. The language model may thenbe modified according to the accented speech file.

BRIEF DESCRIPTION OF THE DRAWINGS

While the specification concludes with claims particularly pointing outand distinctly claiming the present invention, it is believed the samewill be better understood from the following description taken inconjunction with the accompanying drawings, which illustrate, in anon-limiting fashion, the best mode presently contemplated for carryingout the present invention, and in which like reference numeralsdesignate like parts throughout the Figures, wherein:

FIG. 1 shows an architecture view of the system and method for modifyinga language model in accordance with certain teachings of the presentdisclosure; and

FIG. 2 shows an accented speaker list creation in accordance withcertain teachings of the present disclosure.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present disclosure will now be described more fully with referenceto the Figures in which an embodiment of the present disclosure isshown. The subject matter of this disclosure may, however, be embodiedin many different forms and should not be construed as being limited tothe embodiments set forth herein.

Referring to FIG. 1, an architecture view shows the system and methodfor modifying a language model in accordance with certain teachings ofthe present disclosure. The architecture delivers a factory languagemodel 25 to a final step 65. The architecture also provides for thecreation of a site-specific language model 35 and/or a task languagemodel 60 if sufficient data is available. If available, thesite-specific language model and the task language model may bedelivered to the final step 65. In the final step 65, the speechrecognition engine selects the latest or most recently updated languagemodel.

Multiple factory language models are available and each factory languagemodel 25 focuses on a particular area or discipline. Within each factorylanguage model 25, a particular group of words are included to assist intranscribing a dictation. In the preferred embodiment, the factorylanguage model 25 is specific to one discipline of the medicalcommunity, such as radiology, pediatrics, oncology, cardiology, etc.Although the preferred embodiment includes a medical discipline as afactory language model 25, a factory language model on a legaldiscipline, academic discipline or other discipline may be employedwithout deviating from the scope of the invention.

The architecture begins with an initiating step 10 of recognizing aspeech recognition job and providing the factory language model 25 tothree locations in the architecture: the final step 65, the step 15, andthe task language model 60. The step 15 determines if a site-specificword list exists for the creation of a site-specific language model 35.If a site-specific word list exists, then step 20 adds the site-specificwords to the factory language model 25, thus creating a site-specificlanguage model 35. The site-specific language model 35, if available,may then be provided to two locations in the architecture: the finalstep 65 and the task language model 60.

Site-specific word lists may be created by the user or the factory andinclude words unique to a particular site. For example, a radiologyfactory language model may be modified to a site-specific radiologylanguage model by adding the department names of the site hospital andthe names of physicians working at the site. If a site-specific languagemodel 35 is created, then the model is site-specific, usable by anydictators at the site.

FIG. 1 also shows the step 30, which determines if there is an accentedspeech list to apply or if there is sufficient corrected text to createa task language model 60. In the preferred embodiment, the step 45 maydetermine whether the doctor or physician dictating is an accentedspeaker. Step 40 may determine if the doctor has enough corrected textreports 40 to create a task language model 60. If there is no accentedspeaker or new reports available for language model adaptation, then thefactory language model 25 is provided to the final step 65 for possibleuse. While step 15 provides for site level modification, the step 30provides for individual or specific user modification. If a tasklanguage model 60 is created, then the model is user specific, usable bythe individual dictator.

In the case of language model adaptation only, step 55 will run thelanguage model adaptation if sufficient corrected text reports exist tocreate a task language model 60. When no accented speaker or accentedspeech list is available, only the results of the language modeladaptation are provided for creation of the task language model 60. Theresults of the language model adaptation are combined with the factorylanguage model 25 or, if available, the site-specific language model 35to create the task language model 60. The task language model 60 is thenprovided to the final step 65.

In the case of an accented speaker with an accented speech list, theaccented speech list 50 may be used to create the task language model60. When no language model adaptation is available, only the accentedspeech list 50 is combined with the factory language model 25 or, ifavailable, the site-specific language model 35 to create the tasklanguage model 60. The task language model 60 is then provided to thefinal step 65.

In the case of both language model adaptation and an accented speakerbeing available, the accented speech list 50 and the results of thelanguage model adaptation are combined with the factory language model25 or, if available, the site-specific language model 35 to create thetask language model 60, which is a unique and optimized task languagemodel for the particular doctor or user. The task language model 60 isthen provided to the final step 65.

Step 45 may also identify the specific accent of the speaker such thatthe appropriate accented speech list may be added in step 50. Forexample, if the accented speech is determined to be United KingdomEnglish, then a United Kingdom English accented speech list or filewould be used in steps 50 and 60 to create a task language model 60.

In the final step 65 of the modification architecture, the most updatedand specific language model possible is provided for the speechrecognition job. Therefore, if the site-specific language model is morerecent than a factory language model, then the site-specific languagemodel will be used for the job. Likewise, if a task language model isthe most recent language model available at the final step 65, then thetask language model will be used for the job.

Referring to FIG. 2, the creation of an accented speech list starts with100 and creates the accented speech list 50 of FIG. 1. The step 110 mayidentify an accent and the pronunciation differences between theaccented speech pronunciations and the pronunciations of the language ofthe language model.

Step 110 also includes selecting a group of pronunciation differencesbased on a set of pre-determined criteria. The criteria may be used todetermine the pronunciation differences that are best handled by addingspecial pronunciations to the language model. These criteria may includephonemic and subphonemic differences as well as systematic andidiosyncratic criteria.

Systematic rule-governed differences are those which follow a patternand apply to all items that match certain criteria for all speakers of acertain accent. They can be described by deterministic linguistic rulesof the form X→Y/Z_or X→Y/_Z (read: X becomes Y after or before Z). Forexample, for some Indian accented English accents all words beginningwith the letter ‘p’ are pronounced in an unreleased fashion that soundsmore similar to ‘b’ to United States English speakers. A second morefamiliar example is the pronunciation of the letter combination ‘ar’within a syllable. In most United States English dialects, speakerspronounce the ‘r’ such that the words sound approximately like ‘ahr’(although in Boston its more ‘ae’ and in the Bronx its more ‘aw.’); inUnited Kingdom English pronunciation, the ‘r’ is not pronounced suchthat the pronunciation is approximately ‘ah.’ These systematicrule-governed differences may or may not need supplementalpronunciations in the language model to be captured by a speechrecognition engine.

Not every pronunciation difference will require supplemental accentedspeech pronunciations because the language model pronunciations providedby the factory or by the site-specific language models will sufficientlyrecognize some words pronounced with an accent. In this case, theaccented pronunciation differs subphonemically from the language modelpronunciations and therefore the pronunciation differences do not needto be encoded as separate or supplemental pronunciations in the languagemodel. This kind of accented speech pronunciation is probably besthandled by speaker enrollment, possibly in conjunction with acousticmodels specific to the speaker's accent.

However, some subphonemic differences may nonetheless result in phonememerger. Sometimes two differences in pronunciation, which individuallydo not merge phonemic differences, collaborate to merge a phonemicdifference when compared to the factory language models. Supplementalaccented speech pronunciations in the language model may be necessary tohandle these sorts of pronunciation differences. Using the Indianaccented English as an example, syllable-initial p,t,k are aspiratedacross the board in United States English, but never in Indian accentedEnglish. Because the difference between aspirated and unaspirated stopsis subphonemic in United States English, it might be assumed that thatthere is no need for supplemental accent-specific pronunciations in thelanguage models. However, there are two possible problems: unaspirated/p/ sounds like /b/ to the untrained United States English speaker'sear, and for many speakers of Indian accented English, /f/ is pronouncedas an aspirated /p/. Thus, although only allophone is involved here, thedistinction between two distinct phonemes in United States English ismerged. So for such Indian accented speakers, ‘fit’ is likely to bemisidentified as ‘pit’.

Although subphonemic differences may not generally require specialaccent-specific pronunciations, phonemic differences will requirespecial accent-specific pronunciations in the language model. Forexample, one phonemic difference is in the placement of word stress. InEnglish, the placement of word stress can vary greatly across dialects,and this has salient acoustic effects, in part because when a vowel goesfrom being accented to unaccented, its quality changes. For anotherexample, if accented speakers pronounce /t/ where United States Englishspeakers pronounce a ‘th’ sound, ‘breath’ and ‘Brett’ will sound alike.To improve the chances of ‘breath’ being properly recognized, the /bret/accent-specific pronunciation should may be added to the language modelfor the word ‘breath.’

Note that there are too many words in the United States English languagemodels with the ‘th’ sound in them to provide supplementarypronunciations for all of them. However, a word like ‘breath’ is anexcellent example of a word that is likely to be used in medicaldictation. In the preferred embodiment, ‘breath’ might be a goodcandidate for inclusion in the accented-language list.

Morpheme-governed phonemic and stress differences also require specialaccent-specific pronunciations in the language model. Certain suffixesare pronounced differently and trigger different stress patterns inUnited States English as opposed to United Kingdom English. Thesedifferences are defined by whole classes of words whose accentedpronunciations would best be handled by distinct pronunciations. Forexample, in Indian accented English and United Kingdom English, thesuffix ‘-atory’ has a long ‘a’ that has the primary word accent. This isquite different from the suffix ‘-atory’ in United States English.

The above mentioned pronunciation differences are generally systematicrule-governed; that is differences that apply predictably across anentire set or a subset of words in a lexicon based on a rule or set ofrules. However other differences such as unsystematic and idiosyncraticdifferences may also be corrected with supplemental pronunciations.

Unsystematic differences do not follow any specific pattern andunsystematic phonemic and stress differences can create pronunciationdifferences requiring special accent-specific pronunciations in thelanguage model. An example is the word ‘schedule,’ pronounced with a‘sh’ sound in United Kingdom and Indian English but pronounced with a‘sk’ sound in United States English. This difference is unsystematicbecause it does not apply to all words beginning with ‘sch’ (‘schema’ isnot pronounced with ‘sh’). Furthermore, in Indian accented English, theword stress is on the first syllable of ‘developed’ but the stress is onthe second syllable in United Kingdom English and United States English.The discovery of these unsystematic differences is more anecdotal thanother phonemic differences that are rule-governed.

Idiosyncratic differences are those accent pronunciations specific to anindividual. It may be possible that more than one speaker shares anidiosyncratic difference, but an entire group does not share anidiosyncratic difference. Idiosyncratic differences are generally notrule-governed.

In the preferred embodiment, the language model is in United StatesEnglish and the accented speech is United States English spoken with thepronunciations of the user's accent. However, the native language modelcould be in any language and the accented speech could be the nativelanguage spoken with any accent common to a group of individuals.

For any given pronunciation difference in the selected group of step110, step 120 may identify all words in the language models thatinstantiate the pronunciation difference. Step 120 may create a list ofthe identified instantiations.

Step 130 of FIG. 2 determines if it is possible to add the entire listof instantiations and accented speech pronunciations corresponding tothe identified words in the list of instantiations. If so, theinstantiations and corresponding accented speech pronunciations areadded in step 170 to the accented speech file. However, if the list istoo large, steps 140, 150, and 160 can be used to select a subset of thelist of instantiations likely to make the most significant improvementin recognition of the accented speech.

In step 140, a subset of the list of instantiations may be selected byconsidering those words that are most frequently or likely dictatedwords in the list of instantiations. Using an Indian accent and amedical language model as an example, the accented speech would replacethe United States English ‘th’ with ‘t’ and a special pronunciation of‘thyroid’ should be considered for selection in the subset. Likewise,‘theobalda’ could conceivably come up in dictation but considering thatit is not as likely as ‘thyroid’, it should be included only if there isenough space for both.

In step 150, a subset of the list of instantiations may be selected byconsidering those words that are likely to intrude on other words ifthey are not given a supplementary, accented speech pronunciation. Usingthe same Indian accent example, the pronunciation of ‘thick’, pronouncedwith the ‘t’ sound rather than the ‘th’ sound, might well intrude on‘tick’ or ‘tic’ unless ‘thick’ is given an alternate ‘t’ pronunciation.

Pronunciation intrusions may be handled in different ways. One would beto provide new accented pronunciations in the language model for theintruding word and the intruded word. Another method, the method used inthe preferred embodiment, may include the intruding word being given asupplemental pronunciation such that the intruding word and the intrudedword share a pronunciation. For example, the word ‘thick’ and ‘tic’would share the pronunciation ‘tic.’ Statistical methods in the languagemodel could be used to determine which word was intended by an accentedspeaker when the speech recognition engine encounters the pronunciation‘tic.’ Therefore, removing pronunciation intrusions may be accomplishedby providing the intruding word with the shared pronunciation such thatthe intruding and intruded-upon words may be distinguished by meansother than pronunciation (such as language model statistics).

In step 160, a subset of the list of instantiations may be selected byconsidering the length of the words, specifically words that are shortor otherwise more likely to by misidentified. Again using the sameIndian accent example, the word ‘atherosclerosis’ is more likely to berecognized without the benefit of a supplementary pronunciation than‘thick’.

Once a sufficiently sized list or subset of instantiations is foundthrough steps 130, 140, 150, and 160, the words in the list or subset ofinstantiations along with the corresponding accented speechpronunciations are added to the accented speech file in step 170. Oncethe accented speech file is produced, the accented speech file creationis finished in step 180 and the accented speech file can be added to thesite-specific language model in step 50 of FIG. 1.

The accented speech file can also be used to include clones forcorrecting accent-specific phrases or patterns. So, for instance, whendictating parentheses, some accented speakers might say, ‘left paren’,‘left parenthesis’, ‘begin parenthesis’, or ‘begin paren’. There are avariety of ways of dictating the beginning of a pair of parentheses. Byincluding all the pronunciation clones for dictating parentheses, theclones all behave the same way even though they have differentpronunciations. Although in general, the accented speech file is notintended for the purpose of adding language-specific or accent-specificvocabulary words to the United States English language model, there aresome commonly used punctuation words that would provide benefit to anaccented speaker if included in a task language model. For instance,most United Kingdom English and Indian accented English speakers say‘full stop’ instead of ‘period’ at the end of a dictated sentence.Although ‘full stop’ is not in an United States English language model,a clone of ‘period’ could be added to the task language model by theaccented speech file such that an accented speaker could use both‘period’ and ‘full stop’ and still conform to United States Englishdictations norms.

It will be apparent to one of skill in the art that described herein isa novel system and method for modifying a language model. While theinvention has been described with reference to specific preferredembodiments, it is not limited to these embodiments. The invention maybe modified or varied in many ways and such modifications and variationsas would be obvious to one of skill in the art are within the scope andspirit of the invention and are included within the scope of thefollowing claims.

1. A method for modifying a language model, the method comprising the steps of: identifying accented speech pronunciations of words of a language; identifying pronunciation differences between customary speech pronunciations and the accented speech pronunciations; identifying, for each of said pronunciation differences, a first list of words in the language model that instantiate said pronunciation differences; selectively adding the first list of words and their accented speech pronunciations to an accented speech file; and modifying the language model according to the accent speech file.
 2. The method of claim 1, wherein identifying said pronunciation differences excludes subphonemic differences.
 3. The method of claim 1, further comprising: selectively reducing the first list to words that are most frequently used in the language model.
 4. The method of claim 3, further comprising: selectively reducing the first list to words that intrude on other words if they are not given accented speech pronunciations.
 5. The method of claim 4, further comprising: selectively reducing the first list to short words.
 6. The method of claim 5, further comprising: selectively reducing the first list to words with unrecognizable accented speech pronunciations.
 7. The method of claim 1, wherein the modifying the language model includes supplementing the language model pronunciations with accented speech pronunciations.
 8. The method of claim 1, wherein the modifying the language model includes replacing the language model pronunciations with accented speech pronunciations.
 9. The method of claim 1, further comprising: identifying clone pronunciations between customary speech and accented speech; and selectively adding the clone pronunciations to the accented speech file.
 10. A method for modifying a language model, the method comprising the steps of: identifying accented speech pronunciations of a language; identifying pronunciation differences between customary speech pronunciations and the accented speech pronunciations; identifying, for each of said pronunciation differences, words in the language model that instantiate said pronunciation differences; adding said words and said accented speech pronunciations corresponding to said words to an accented speech file according to a predetermined category; and modifying the language model according to the accent speech file.
 11. The method of claim 10, wherein identifying said pronunciation differences excludes subphonemic differences.
 12. The method of claim 10, wherein said predetermined category being all said words.
 13. The method of claim 10, wherein said predetermined category being the most frequently used words in the language model.
 14. The method of claim 10, wherein said predetermined category being the words intruding on other words if they are not given accented speech pronunciations.
 15. The method of claim 10, wherein said predetermined category being short words.
 16. The method of claim 10, wherein said predetermined category being the words with unrecognizable accented speech pronunciations.
 17. The method of claim 10, wherein the modifying the language model includes supplementing the language model pronunciations with accented speech pronunciations.
 18. The method of claim 10, wherein the modifying the language model includes replacing the language model pronunciations with accented speech pronunciations.
 19. The method of claim 10, further comprising: identifying clone pronunciations between customary speech and accented speech; and selectively adding the clone pronunciations to the accented speech file.
 20. A method for customizing a language model for accented speakers, the method comprising the steps of: identifying an accent; determining pronunciation differences between the identified accent and the language model; selecting a first subset of the pronunciation differences based on a first set of pre-determined criteria; listing a first set of instantiations based on said first subset; compiling an accent speech word list from the first set of instantiations; determining accent-specific pronunciations corresponding to words in the accent speech word list; and applying the accented speech word list and the accent-specific pronunciations to the language model.
 21. The method of claim 20, wherein determining the pronunciation differences includes system rule governed differences.
 22. The method of claim 20, wherein the first set of pre-determined criteria is phonemic differences.
 23. The method of claim 20, wherein the first set of pre-determined criteria is idiosyncratic criteria.
 24. The method of claim 20, wherein compiling an accent speech word list from the first set of instantiations is based on a second set of pre-determined criteria.
 25. The method of claim 24, wherein the second set of pre-determined criteria includes at least one of word frequency, pronunciation intrusions, and word length.
 26. The method of claim 25, wherein pronunciation intrusions are based on a third set of pre-determined criteria.
 27. The method of claim 24, wherein the second set of pre-determined criteria includes pronunciation intrusion wherein intruding and intruded-upon words may be distinguished by means other than pronunciation.
 28. The method of claim 20, further comprising: identifying clone pronunciations between customary speech and accented speech; and selectively adding the clone pronunciations to the accent speech word file.
 29. A method for modifying a language model, the method comprising the steps of: identifying accented speech pronunciations of words of a language; identifying pronunciation differences between customary speech pronunciations and the accented speech pronunciations; identifying, for each of said pronunciation differences, a first list of words in the language model that instantiate said pronunciation differences; selectively adding the first list of words and their accented speech pronunciations to an accented speech file; selectively reducing the first list to a second list of words that are most frequently used in the language model; selectively adding the second list of words and their accented speech pronunciations to the accented speech file; selectively reducing the second list to a third list of words, wherein said third list includes words that intrude on other words if they are not given accented speech pronunciations; selectively adding the third list of words and their accented speech pronunciations to the accented speech file; selectively reducing the third list to a forth list of short words; selectively adding the fourth list of words and their accented speech pronunciations to the accented speech file; selectively reducing the fourth list to a fifth list of words with unrecognizable accented speech pronunciations; selectively adding the fifth list of words and their accented speech pronunciations to the accented speech file; and modifying the language model according to the accented speech file. 